Professor Thomas Lumley

Chair in Biostatistics

In: Statistics » Faculty of Science

Biography

I attended Monash University (B.Sc.(Hons) in Pure Mathematics), the University of Oxford (M.Sc. in Applied Statistics) and the University of Washington, Seattle (PhD in Biostatistics). I spent twelve years on the faculty of the Department of Biostatistics at the University of Washington, and then moved to Auckland in 2010. I am still an Affiliate Professor at the University of Washington.

Research | Current

My research interests include

Semiparametric models
Survey sampling
Statistical computing
Foundations of statistics
and whatever methodological problems his medical collaborators come up with -- currently, multiple imputation on big datasets

Resources

The survey package for R is a fairly comprehensive system for analysis of data from complex probability samples.
I have written a book on survey analysis, published by Wiley.

Teaching | Current

STATS 369 S2 2018
STATS 763 S1 2019

Postgraduate supervision

Potential student projects

Likelihood of the empirical distribution function as an approach to Bayesian analysis of survey data.
Mixed models under complex sampling -- and maybe network models, too
Survey software: design and implementation of various things for the survey package in R or for database backends. Graphics, probability distributions, regression models, multivariate methods...
Software for teaching:design and implementation of interface to R for teaching introductory biostatistics
How many categories? Data on, say, hospital visits, can be categorized coarsely (eg lung problem) or much more finely (eg infection by pencillin-resistant Strep. pneumoniae). When the categories are too fine the sample size in each one is too small to see patterns; when they are too coarse, the patterns are masked by events that don't really belong. The idea is to build a tree structure that uses all levels of categorization simultaneously in a Bayesian model.

Distinctions/Honours

Fellow of the Royal Society of New Zealand
Fellow of the American Statistical Association

Selected publications and creative works (Research Outputs)

Lumley, T. (2019). Fast Generalized Linear Models by Database Sampling and One-Step Polishing. JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS10.1080/10618600.2019.1610312
Oh, E. J., Shepherd, B. E., Lumley, T., & Shaw, P. A. (2018). Considerations for analysis of time-to-event outcomes measured with error: Bias and correction with SIMEX. Statistics in medicine, 37 (8), 1276-1289. 10.1002/sim.7554
Rice, K., Higgins, J. P. T., & Lumley, T. (2018). A re-evaluation of fixed effect(s) meta-analysis. Journal of the Royal Statistical Society. Series A: Statistics in Society, 181 (1), 205-227. 10.1111/rssa.12275
Lumley, T., & Scott, A. (2017). Fitting Regression Models to Survey Data. Statistical Science, 32 (2), 265-278. 10.1214/16-STS605
Rivera, C. L., & Lumley, T. (2016). Using the entire history in the analysis of nested case cohort samples. Statistics in medicine, 35 (18), 3213-3228. 10.1002/sim.6917
Other University of Auckland co-authors: Claudia Rivera Rodriguez
Rivera, C., & Lumley, T. (2016). Using the whole cohort in the analysis of countermatched samples. Biometrics, 72 (2), 382-391. 10.1111/biom.12419
Other University of Auckland co-authors: Claudia Rivera Rodriguez
Conomos, M. P., Laurie, C. A., Stilp, A. M., Gogarten, S. M., McHugh, C. P., Nelson, S. C., ... Graff, M. (2016). Genetic diversity and association studies in US Hispanic/Latino populations: Applications in the Hispanic Community Health Study/Study of Latinos. American Journal of Human Genetics, 98 (1), 165-184. 10.1016/j.ajhg.2015.12.001
Lumley, T., & Scott, A. (2015). AIC and BIC for modeling with complex survey data. Journal of Survey Statistics and Methodology, 3 (1), 1-18. 10.1093/jssam/smu021